深度学习-Pytorch：Pytorch 创建CNN神经网络模型【ResNet模型】

古城微笑少年丶 2023-10-05 20:05 87阅读 0赞

## 一、自定义ResNet神经网络-Pytorch【cifar10图片分类数据集】 ##

import torch
    from torch.utils.data import DataLoader
    from torchvision import datasets
    from torchvision import transforms
    from torch import nn, optim
    from torch.nn import functional as F
    
    
    # 两层的残差学习单元 BasicBlock [(3×3)-->(3×3)]形状，如果是三层的BasicBlock，形状则为：[(1×1)-->(3×3)-->(1×1)]
    # filter_count_in≠filter_count_out时，则通过该层Layer后的FeatureMap的大小改变，identity层也需要reshape
    class BasicBlock(nn.Module):
        def __init__(self, filter_count_in, filter_count_out, stride=1):
            super(BasicBlock, self).__init__()
            # we add stride support for resbok, which is distinct from tutorials.
            self.conv1 = nn.Conv2d(in_channels=filter_count_in, out_channels=filter_count_out, kernel_size=3, stride=stride, padding=1)
            self.bn1 = nn.BatchNorm2d(filter_count_out)
            self.conv2 = nn.Conv2d(filter_count_out, filter_count_out, kernel_size=3, stride=1, padding=1)
            self.bn2 = nn.BatchNorm2d(filter_count_out)
            self.identity = nn.Sequential()
            if filter_count_in != filter_count_out:  # 将输入值x的维度调整为和F(x)的输出维度保持一致  [b, filter_count_in, h, w] => [b, filter_count_out, h, w]
                self.identity = nn.Sequential(
                    nn.Conv2d(filter_count_in, filter_count_out, kernel_size=1, stride=stride),
                    nn.BatchNorm2d(filter_count_out)
                )
    
        def forward(self, input):
            x = self.conv1(input)
            x = self.bn1(x)
            x = F.relu(x)
            x = self.conv2(x)
            F_out = self.bn2(x)
            # short cut
            identity_out = self.identity(input)  # 调整input的维度与F_out保持一致，然后才能和F_out相加：[b, ch_in, h, w] => [b, ch_out, h, w]
            H_out = identity_out + F_out
            H_out = F.relu(H_out)
    
            return H_out
    
    
    # 由多个BasicBlock组成的ResidualBlock
    class ResidualBlock:
        def __init__(self, filter_count_in, filter_count_out, residualBlock_size=1, stride=1):
            self.filter_count_in = filter_count_in
            self.filter_count_out = filter_count_out
            self.residualBlock_size = residualBlock_size
            self.stride = stride
    
        def __call__(self):
            basic_block_stride_eq = BasicBlock(self.filter_count_in, self.filter_count_in, stride=1)  # stride = 1 时的BasicBlock H(x)=x+F(X)，identity_layer层的输出为直接返回输入
            basic_block_stride_not_eq = BasicBlock(self.filter_count_in, self.filter_count_out, stride=self.stride)  # stride != 1 时的BasicBlock H(x)=x+F(X)，identity_layer进行SubSampling
            residualBlock = nn.Sequential()
            for _ in range(0, self.residualBlock_size - 1):  # 其余的BasicBlock都是 filter_count_in == filter_count_out 时的BasicBlock
                residualBlock.add_module('basic_block_stride_eq', basic_block_stride_eq)
            residualBlock.add_module('basic_block_stride_not_eq', basic_block_stride_not_eq)  # 有一个BasicBlock必须是 filter_count_in != filter_count_out 时的BasicBlock
            return residualBlock
    
    
    # 由多个ResidualBlock组成的ResidualNet
    class ResNet18(nn.Module):
        def __init__(self):
            super(ResNet18, self).__init__()
            self.conv1 = nn.Sequential(
                nn.Conv2d(3, 64, kernel_size=3, stride=3, padding=0),
                nn.BatchNorm2d(64)
            )
            # followed 4 ResidualBlock
            self.residualBlock1 = ResidualBlock(filter_count_in=64, filter_count_out=128, residualBlock_size=2, stride=2)()  # [b, 64, h, w] => [b, 128, h ,w]
            self.residualBlock2 = ResidualBlock(filter_count_in=128, filter_count_out=256, residualBlock_size=2, stride=2)()  # [b, 128, h, w] => [b, 256, h, w]
            self.residualBlock3 = ResidualBlock(filter_count_in=256, filter_count_out=512, residualBlock_size=2, stride=2)()  # [b, 256, h, w] => [b, 512, h, w]
            self.residualBlock4 = ResidualBlock(filter_count_in=512, filter_count_out=512, residualBlock_size=2, stride=2)()  # [b, 512, h, w] => [b, 1024, h, w]
            self.outlayer = nn.Linear(512 * 1 * 1, 10)
    
        def forward(self, X):
            X = F.relu(self.conv1(X))
            # [b, 64, h, w] => [b, 1024, h, w]
            X = self.residualBlock1(X)
            X = self.residualBlock2(X)
            X = self.residualBlock3(X)
            X = self.residualBlock4(X)  # [b, 512, 2, 2]
            X = F.adaptive_avg_pool2d(X, [1, 1])  # [b, 512, 2, 2] => [b, 512, 1, 1]
            X = X.view(X.size(0), -1)  # [b, 512, 1, 1] => [b, 512]
            X = self.outlayer(X)  # [b, 512] => [b, 10]
    
            return X
    
    
    def main():
        batch_size = 200
        # 一、获取cifar10训练数据集
        cifar_train = datasets.CIFAR10('cifar', True, transform=transforms.Compose([
            transforms.Resize((32, 32)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])
        ]), download=True)
        cifar_train = DataLoader(cifar_train, batch_size=batch_size, shuffle=True)
        cifar_test = datasets.CIFAR10('cifar', False, transform=transforms.Compose([
            transforms.Resize((32, 32)),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])
        ]), download=True)
        cifar_test = DataLoader(cifar_test, batch_size=batch_size, shuffle=True)
    
        # 二、设置GPU
        device = torch.device('cuda')
    
        # 三、实例化ResNet18神经网络模型
        model = ResNet18().to(device)
        # Find total parameters and trainable parameters
        total_params = sum(p.numel() for p in model.parameters())
        print(f'{total_params:,} total parameters.')
        total_trainable_params = sum(
            p.numel() for p in model.parameters() if p.requires_grad)
        print(f'{total_trainable_params:,} training parameters.')
        print('model = {0}\n'.format(model))
    
        # 四、实例化损失函数
        criteon = nn.CrossEntropyLoss().to(device)
    
        # 五、梯度下降优化器设置
        optimizer = optim.Adam(model.parameters(), lr=1e-3)
    
        # 六、训练
        for epoch in range(3):
            # **********************************************************训练**********************************************************
            print('**************************训练模式：开始**************************')
            model.train()  # 切换至训练模式
            for batch_index, (X_batch, Y_batch) in enumerate(cifar_train):
                X_batch, Y_batch = X_batch.to(device), Y_batch.to(device)
                out_logits = model(X_batch)
                loss = criteon(out_logits, Y_batch)
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()
                if batch_index % 100 == 0:
                    print('epoch = {0}, batch_index = {1}, loss.item() = {2}'.format(epoch, batch_index, loss.item()))
            print('**************************训练模式：结束**************************')
            # **********************************************************模型评估**********************************************************
            print('**************************验证模式：开始**************************')
            model.eval()  # 切换至验证模式
            with torch.no_grad():  # torch.no_grad()所包裹的部分不需要参与反向传播
                # test
                total_correct = 0
                total_num = 0
                for batch_index, (X_batch, Y_batch) in enumerate(cifar_test):
                    X_batch, Y_batch = X_batch.to(device), Y_batch.to(device)
                    out_logits = model(X_batch)
                    out_pred = out_logits.argmax(dim=1)
                    correct = torch.eq(out_pred, Y_batch).float().sum().item()
                    total_correct += correct
                    total_num += X_batch.size(0)
                    acc = total_correct / total_num
                    if batch_index % 100 == 0:
                        print('epoch = {0}, batch_index = {1}, test acc = {2}'.format(epoch, batch_index, acc))
            print('**************************验证模式：结束**************************')
    
    
    if __name__ == '__main__':
        main()

打印结果：

Files already downloaded and verified
    Files already downloaded and verified
    15,826,314 total parameters.
    15,826,314 training parameters.
    model = ResNet18(
      (conv1): Sequential(
        (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(3, 3))
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (residualBlock1): Sequential(
        (basic_block_stride_eq): BasicBlock(
          (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (basic_block_stride_not_eq): BasicBlock(
          (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (identity): Sequential(
            (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2))
            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
      )
      (residualBlock2): Sequential(
        (basic_block_stride_eq): BasicBlock(
          (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (basic_block_stride_not_eq): BasicBlock(
          (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (identity): Sequential(
            (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2))
            (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
      )
      (residualBlock3): Sequential(
        (basic_block_stride_eq): BasicBlock(
          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (basic_block_stride_not_eq): BasicBlock(
          (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (identity): Sequential(
            (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2))
            (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
      )
      (residualBlock4): Sequential(
        (basic_block_stride_eq): BasicBlock(
          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (basic_block_stride_not_eq): BasicBlock(
          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (outlayer): Linear(in_features=512, out_features=10, bias=True)
    )
    
    **************************训练模式：开始**************************
    epoch = 0, batch_index = 0, loss.item() = 2.784912109375
    epoch = 0, batch_index = 100, loss.item() = 1.2591865062713623
    epoch = 0, batch_index = 200, loss.item() = 1.2418736219406128
    **************************训练模式：结束**************************
    **************************验证模式：开始**************************
    epoch = 0, batch_index = 0, test acc = 0.515
    **************************验证模式：结束**************************
    **************************训练模式：开始**************************
    epoch = 1, batch_index = 0, loss.item() = 1.0537413358688354
    epoch = 1, batch_index = 100, loss.item() = 1.088006615638733
    epoch = 1, batch_index = 200, loss.item() = 1.0332653522491455
    **************************训练模式：结束**************************
    **************************验证模式：开始**************************
    epoch = 1, batch_index = 0, test acc = 0.635
    **************************验证模式：结束**************************
    **************************训练模式：开始**************************
    epoch = 2, batch_index = 0, loss.item() = 0.9080470204353333
    epoch = 2, batch_index = 100, loss.item() = 0.7950635552406311
    epoch = 2, batch_index = 200, loss.item() = 0.7487978339195251
    **************************训练模式：结束**************************
    **************************验证模式：开始**************************
    epoch = 2, batch_index = 0, test acc = 0.64
    **************************验证模式：结束**************************
    
    Process finished with exit code 0

## 二、自定义ResNet18 & 自定义数据集-Pytorch ##

import torch
    from torch.utils.data import DataLoader
    from torch import nn, optim
    from torch.nn import functional as F
    import visdom
    import csv
    import glob
    import os
    import random
    from PIL import Image
    from torch.utils.data import Dataset  # 自定义数据集的父类
    from torchvision import transforms
    
    torch.manual_seed(1234)  # 随机种子
    device = torch.device('cuda')  # 设置GPU
    
    
    # =============================================================================Pokemon自定义数据集：开始=============================================================================
    class Pokemon(Dataset):
        # root表示数据位置；resize表示数据输出的size；mode表示训练模式/测试模式
        def __init__(self, root, resize, mode):
            super(Pokemon, self).__init__()
            self.root = root
            self.resize = resize
    
            # 给各个类型进行编号
            self.name2label = {
        }  # {'bulbasaur': 0, 'charmander': 1, 'mewtwo': 2, 'pikachu': 3, 'squirtle': 4}
            for name in sorted(os.listdir(os.path.join(root))):
                if not os.path.isdir(os.path.join(root, name)):  # 过滤掉不是文件夹的文件
                    continue
                self.name2label[name] = len(self.name2label.keys())
            print('self.name2label = {0}'.format(self.name2label))  # {'bulbasaur': 0, 'charmander': 1, 'mewtwo': 2, 'pikachu': 3, 'squirtle': 4}
    
            # 读取已保存的图片+标签数据集
            self.img_paths, self.labels = self.load_csv('img_paths.csv')  # 数据对(img_path + image_label)：img_paths, labels
            # 对数据集根据当前模式进行裁剪
            if mode == 'train':  # 60%
                self.img_paths = self.img_paths[:int(0.6 * len(self.img_paths))]
                self.labels = self.labels[:int(0.6 * len(self.labels))]
            elif mode == 'val':  # 20% = 60%->80%
                self.img_paths = self.img_paths[int(0.6 * len(self.img_paths)):int(0.8 * len(self.img_paths))]
                self.labels = self.labels[int(0.6 * len(self.labels)):int(0.8 * len(self.labels))]
            else:  # 20% = 80%->100%
                self.img_paths = self.img_paths[int(0.8 * len(self.img_paths)):]
                self.labels = self.labels[int(0.8 * len(self.labels)):]
    
        def load_csv(self, filename):
            # 1、如果没有csv文件，则创建该csv文件
            if not os.path.exists(os.path.join(self.root, filename)):
                img_paths = []  # 把所有图片的path都保存在该list中，各个图片的label可以从path推断出来，所有没有单独保存。
                for name in self.name2label.keys():
                    img_paths += glob.glob(os.path.join(self.root, name, '*.png'))  # 'pokemon\\mewtwo\\00001.png
                    img_paths += glob.glob(os.path.join(self.root, name, '*.jpg'))
                    img_paths += glob.glob(os.path.join(self.root, name, '*.jpeg'))
                    img_paths += glob.glob(os.path.join(self.root, name, '*.gif'))
                print('len(img_paths) = {0}, img_paths = {1}'.format(len(img_paths), img_paths))  # len(img_paths) = 1168, img_paths = ['pokemon\\bulbasaur\\00000000.png','pokemon\\bulbasaur\\00000001.png',...]
                random.shuffle(img_paths)  # 打乱list中的图片顺序
                # 向csv文件保存图片的path+label
                with open(os.path.join(self.root, filename), mode='w', newline='') as f:
                    writer = csv.writer(f)
                    for img_path in img_paths:  # 'pokemon\\bulbasaur\\00000000.png'
                        name = img_path.split(os.sep)[-2]
                        label = self.name2label[name]
                        writer.writerow([img_path, label])  # 'pokemon\\bulbasaur\\00000000.png', 0
                    print('writen into csv file:', filename)
            # 2、如果已经有csv文件，则读取该csv文件
            img_paths, labels = [], []
            with open(os.path.join(self.root, filename)) as f:
                reader = csv.reader(f)
                for row in reader:
                    img_path, label = row  # 'pokemon\\bulbasaur\\00000000.png', 0
                    label = int(label)
                    img_paths.append(img_path)
                    labels.append(label)
            assert len(img_paths) == len(labels)
            return img_paths, labels
    
        def __len__(self):
            return len(self.img_paths)
    
        def denormalize(self, x_hat):
            mean = [0.485, 0.456, 0.406]
            std = [0.229, 0.224, 0.225]
            # x_hat = (x-mean)/std
            # x = x_hat*std = mean
            # x: [c, h, w]
            # mean: [3] => [3, 1, 1]
            mean = torch.tensor(mean).unsqueeze(1).unsqueeze(1)
            std = torch.tensor(std).unsqueeze(1).unsqueeze(1)
            print('denormalize-->mean.shape = {0}, std.shape = {1}'.format(mean.shape, std.shape))
            x = x_hat * std + mean
    
            return x
    
        def __getitem__(self, img_idx):  # img_idx~[0~len(img_paths)]
            img_path, label = self.img_paths[img_idx], self.labels[img_idx]  # img_path: 'pokemon\\bulbasaur\\00000000.png'；label: 0
            transform = transforms.Compose([
                lambda x: Image.open(x).convert('RGB'),  # string path --> image data
                transforms.Resize((int(self.resize * 1.25), int(self.resize * 1.25))),
                transforms.RandomRotation(15),  # rotate如果比较大的话，可能会造成网络不收敛
                transforms.CenterCrop(self.resize),
                transforms.ToTensor(),
                transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # 该数值是实践中统计的效果比较好的值
            ])
            img = transform(img_path)
            label = torch.tensor(label)
    
            return img, label
    
    
    # =============================================================================Pokemon自定义数据集：结束=============================================================================
    
    
    # =============================================================================ResNet18神经网络：开始=============================================================================
    # 两层的残差学习单元 BasicBlock [(3×3)-->(3×3)]形状，如果是三层的BasicBlock，形状则为：[(1×1)-->(3×3)-->(1×1)]
    # filter_count_in≠filter_count_out时，则通过该层Layer后的FeatureMap的大小改变，identity层也需要reshape
    class BasicBlock(nn.Module):
        def __init__(self, filter_count_in, filter_count_out, stride=1):
            super(BasicBlock, self).__init__()
            self.filter_count_in = filter_count_in
            self.filter_count_out = filter_count_out
            self.stride = stride
            # we add stride support for resbok, which is distinct from tutorials.
            self.conv1 = nn.Conv2d(in_channels=filter_count_in, out_channels=filter_count_out, kernel_size=3, stride=stride, padding=1)
            self.bn1 = nn.BatchNorm2d(filter_count_out)
            self.conv2 = nn.Conv2d(filter_count_out, filter_count_out, kernel_size=3, stride=1, padding=1)
            self.bn2 = nn.BatchNorm2d(filter_count_out)
            self.identity = nn.Sequential()
            if filter_count_in != filter_count_out:  # 将输入值x的维度调整为和F(x)的输出维度保持一致  [b, filter_count_in, h, w] => [b, filter_count_out, h, w]
                self.identity = nn.Sequential(
                    nn.Conv2d(filter_count_in, filter_count_out, kernel_size=1, stride=stride),
                    nn.BatchNorm2d(filter_count_out)
                )
    
        def forward(self, input):
            x = self.conv1(input)
            x = self.bn1(x)
            x = F.relu(x)
            x = self.conv2(x)
            F_out = self.bn2(x)
            # short cut
            identity_out = self.identity(input)  # 调整input的维度与F_out保持一致，然后才能和F_out相加：[b, ch_in, h, w] => [b, ch_out, h, w]
            # print('stride = {0}，filter_count_in = {1}，filter_count_out = {2}，F_out.shape = {3}，identity_out.shape = {4}'.format(self.stride, self.filter_count_in, self.filter_count_out, F_out.shape, identity_out.shape))
            H_out = identity_out + F_out
            H_out = F.relu(H_out)
    
            return H_out
    
    
    # 由多个BasicBlock组成的ResidualBlock
    class ResidualBlock:
        def __init__(self, filter_count_in, filter_count_out, residualBlock_size=1, stride=1):
            self.filter_count_in = filter_count_in
            self.filter_count_out = filter_count_out
            self.residualBlock_size = residualBlock_size
            self.stride = stride
    
        def __call__(self):
            basic_block_stride_eq = BasicBlock(self.filter_count_in, self.filter_count_in, stride=1)  # stride = 1 时的BasicBlock H(x)=x+F(X)，identity_layer层的输出为直接返回输入
            basic_block_stride_not_eq = BasicBlock(self.filter_count_in, self.filter_count_out, stride=self.stride)  # stride != 1 时的BasicBlock H(x)=x+F(X)，identity_layer进行SubSampling
            residualBlock = nn.Sequential()
            for _ in range(0, self.residualBlock_size - 1):  # 其余的BasicBlock都是 filter_count_in == filter_count_out 时的BasicBlock
                residualBlock.add_module('basic_block_stride_eq', basic_block_stride_eq)
            residualBlock.add_module('basic_block_stride_not_eq', basic_block_stride_not_eq)  # 有一个BasicBlock必须是 filter_count_in != filter_count_out 时的BasicBlock
            return residualBlock
    
    
    # 由多个ResidualBlock组成的ResidualNet
    class ResNet18(nn.Module):
        def __init__(self, num_class):  # num_class 表示最终所有分类数量
            super(ResNet18, self).__init__()
            self.conv1 = nn.Sequential(
                nn.Conv2d(3, 64, kernel_size=3, stride=3, padding=0),
                nn.BatchNorm2d(64)
            )
            # followed 4 ResidualBlock
            self.residualBlock1 = ResidualBlock(filter_count_in=64, filter_count_out=128, residualBlock_size=2, stride=2)()  # [b, 64, h, w] => [b, 128, h ,w]
            self.residualBlock2 = ResidualBlock(filter_count_in=128, filter_count_out=256, residualBlock_size=2, stride=2)()  # [b, 128, h, w] => [b, 256, h, w]
            self.residualBlock3 = ResidualBlock(filter_count_in=256, filter_count_out=512, residualBlock_size=2, stride=2)()  # [b, 256, h, w] => [b, 512, h, w]
            self.residualBlock4 = ResidualBlock(filter_count_in=512, filter_count_out=512, residualBlock_size=2, stride=1)()  # [b, 512, h, w] => [b, 1024, h, w]
            self.outlayer = nn.Linear(512 * 1 * 1, num_class)
    
        def forward(self, X):
            X = F.relu(self.conv1(X))
            # [b, 64, h, w] => [b, 1024, h, w]
            X = self.residualBlock1(X)
            X = self.residualBlock2(X)
            X = self.residualBlock3(X)
            X = self.residualBlock4(X)  # [b, 512, 2, 2]
            X = F.adaptive_avg_pool2d(X, [1, 1])  # [b, 512, 2, 2] => [b, 512, 1, 1]
            X = X.view(X.size(0), -1)  # [b, 512, 1, 1] => [b, 512]
            X = self.outlayer(X)  # [b, 512] => [b, 5]
    
            return X
    
    
    # =============================================================================ResNet18神经网络：结束=============================================================================
    
    # =============================================================================训练主体：开始=============================================================================
    batch_size = 32
    viz = visdom.Visdom()  # 在控制台开启Visdom：python -m visdom.server
    global_step = 0
    
    # 一、获取Pokemon训练数据集
    train_db = Pokemon('pokemon', 224, mode='train')
    val_db = Pokemon('pokemon', 224, mode='val')
    test_db = Pokemon('pokemon', 224, mode='test')
    train_loader = DataLoader(train_db, batch_size=batch_size, shuffle=True, num_workers=0)  # num_workers表示开启的线程数量
    val_loader = DataLoader(val_db, batch_size=batch_size, num_workers=0)
    test_loader = DataLoader(test_db, batch_size=batch_size, num_workers=0)
    
    # 三、实例化ResNet18神经网络模型
    model = ResNet18(5).to(device)
    total_params = sum(p.numel() for p in model.parameters())	# 模型参数总数量
    print("模型参数总数量 = {0}".format(total_params ))
    total_trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)	# 模型可训练参数总数量
    print("模型可训练参数总数量 = {0}".format(total_trainable_params )) # print(f'{total_trainable_params:,} training parameters.')
    print('model = {0}\n'.format(model))
    
    # 四、实例化损失函数
    criteon = nn.CrossEntropyLoss().to(device)
    
    # 五、梯度下降优化器设置
    optimizer = optim.Adam(model.parameters(), lr=1e-3)
    
    
    def train_epoch(epoch_no):
        global global_step
        print('++++++++++++++++++++++++++++++++++++++++++++第{0}轮Epoch-->Training 阶段：开始++++++++++++++++++++++++++++++++++++++++++++'.format(epoch_no))
        model.train()  # 切换至训练模式
        for batch_index, (X_batch, Y_batch) in enumerate(train_loader):
            X_batch, Y_batch = X_batch.to(device), Y_batch.to(device)
            out_logits = model(X_batch)
            loss = criteon(out_logits, Y_batch)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            viz.line([loss.item()], [global_step], win='loss', update='append')
            global_step += 1
            if batch_index % 5 == 0:
                print('epoch_no = {0}, batch_index = {1}, loss.item() = {2}'.format(epoch_no, batch_index, loss.item()))
        print('++++++++++++++++++++++++++++++++++++++++++++第{0}轮Epoch-->Training 阶段：结束++++++++++++++++++++++++++++++++++++++++++++'.format(epoch_no))
    
    
    def evalute(epoch_no, loader):
        print('++++++++++++++++++++++++++++++++++++++++++++第{0}轮Epoch-->Evluation 阶段：开始++++++++++++++++++++++++++++++++++++++++++++'.format(epoch_no))
        model.eval()
        with torch.no_grad():
            total_correct = 0
            total_num = 0
            for batch_index, (X_batch, Y_batch) in enumerate(loader):
                X_batch, Y_batch = X_batch.to(device), Y_batch.to(device)
                out_logits = model(X_batch)
                out_pred = out_logits.argmax(dim=1)
                correct = torch.eq(out_pred, Y_batch).float().sum().item()
                total_correct += correct
                total_num += X_batch.size(0)
                val_acc = total_correct / total_num
                viz.line([val_acc], [global_step], win='val_acc', update='append')
                if batch_index % 5 == 0:
                    print('epoch_no = {0}, batch_index = {1}, val_acc = {2}'.format(epoch_no, batch_index, val_acc))
        print('++++++++++++++++++++++++++++++++++++++++++++第{0}轮Epoch-->Evluation 阶段：结束++++++++++++++++++++++++++++++++++++++++++++'.format(epoch_no))
        return val_acc
    
    
    def main():
        epoch_count = 4  # epoch_count为整体数据集迭代梯度下降次数
        best_acc, best_epoch = 0, 0
        viz.line([0], [-1], win='loss', opts=dict(title='loss'))
        viz.line([0], [-1], win='val_acc', opts=dict(title='val_acc'))
        for epoch_no in range(1, epoch_count + 1):
            print('\n\n利用整体数据集进行模型的第{0}轮Epoch迭代开始:**********************************************************************************************************************************'.format(epoch_no))
            train_epoch(epoch_no)  # 训练
            val_acc = evalute(epoch_no, val_loader)  # 验证
            if val_acc > best_acc:
                best_epoch = epoch_no
                best_acc = val_acc
                torch.save(model.state_dict(), 'best.mdl')
            print('epoch = {0}, best_epoch = {1}, best_acc = {2}'.format(epoch_no, best_epoch, best_acc))
            print('**************************验证模式：结束**************************')
            print('利用整体数据集进行模型的第{0}轮Epoch迭代结束:**********************************************************************************************************************************'.format(epoch_no))
        print('best acc:', best_acc, 'best epoch:', best_epoch)
        model.load_state_dict(torch.load('best.mdl'))
        print('loaded from ckpt!')
        test_acc = evalute(best_epoch, test_loader)  # 测试
        print('test acc:', test_acc)
    
    
    if __name__ == '__main__':
        main()
    
    # =============================================================================训练主体：结束=============================================================================

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE_size_16_color_FFFFFF_t_70_pic_center]

打印结果：

Setting up a new session...
    self.name2label = {
        'bulbasaur': 0, 'charmander': 1, 'mewtwo': 2, 'pikachu': 3, 'squirtle': 4}
    self.name2label = {
        'bulbasaur': 0, 'charmander': 1, 'mewtwo': 2, 'pikachu': 3, 'squirtle': 4}
    self.name2label = {
        'bulbasaur': 0, 'charmander': 1, 'mewtwo': 2, 'pikachu': 3, 'squirtle': 4}
    15,823,749 total parameters.
    15,823,749 training parameters.
    model = ResNet18(
      (conv1): Sequential(
        (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(3, 3))
        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
      (residualBlock1): Sequential(
        (basic_block_stride_eq): BasicBlock(
          (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (identity): Sequential()
        )
        (basic_block_stride_not_eq): BasicBlock(
          (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (identity): Sequential(
            (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2))
            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
      )
      (residualBlock2): Sequential(
        (basic_block_stride_eq): BasicBlock(
          (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (identity): Sequential()
        )
        (basic_block_stride_not_eq): BasicBlock(
          (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (identity): Sequential(
            (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2))
            (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
      )
      (residualBlock3): Sequential(
        (basic_block_stride_eq): BasicBlock(
          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (identity): Sequential()
        )
        (basic_block_stride_not_eq): BasicBlock(
          (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (identity): Sequential(
            (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2))
            (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
      )
      (residualBlock4): Sequential(
        (basic_block_stride_eq): BasicBlock(
          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (identity): Sequential()
        )
        (basic_block_stride_not_eq): BasicBlock(
          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (identity): Sequential()
        )
      )
      (outlayer): Linear(in_features=512, out_features=5, bias=True)
    )
    
    
    
    利用整体数据集进行模型的第1轮Epoch迭代开始:**********************************************************************************************************************************
    ++++++++++++++++++++++++++++++++++++++++++++第1轮Epoch-->Training 阶段：开始++++++++++++++++++++++++++++++++++++++++++++
    epoch_no = 1, batch_index = 0, loss.item() = 1.939097285270691
    epoch_no = 1, batch_index = 5, loss.item() = 1.332801342010498
    epoch_no = 1, batch_index = 10, loss.item() = 1.3339236974716187
    epoch_no = 1, batch_index = 15, loss.item() = 0.44973278045654297
    epoch_no = 1, batch_index = 20, loss.item() = 0.4216762185096741
    ++++++++++++++++++++++++++++++++++++++++++++第1轮Epoch-->Training 阶段：结束++++++++++++++++++++++++++++++++++++++++++++
    ++++++++++++++++++++++++++++++++++++++++++++第1轮Epoch-->Evluation 阶段：开始++++++++++++++++++++++++++++++++++++++++++++
    epoch_no = 1, batch_index = 0, val_acc = 0.6875
    epoch_no = 1, batch_index = 5, val_acc = 0.7395833333333334
    ++++++++++++++++++++++++++++++++++++++++++++第1轮Epoch-->Evluation 阶段：结束++++++++++++++++++++++++++++++++++++++++++++
    epoch = 1, best_epoch = 1, best_acc = 0.7478632478632479
    **************************验证模式：结束**************************
    利用整体数据集进行模型的第1轮Epoch迭代结束:**********************************************************************************************************************************
    
    
    利用整体数据集进行模型的第2轮Epoch迭代开始:**********************************************************************************************************************************
    ++++++++++++++++++++++++++++++++++++++++++++第2轮Epoch-->Training 阶段：开始++++++++++++++++++++++++++++++++++++++++++++
    epoch_no = 2, batch_index = 0, loss.item() = 0.5493289232254028
    epoch_no = 2, batch_index = 5, loss.item() = 0.6154159307479858
    epoch_no = 2, batch_index = 10, loss.item() = 0.6554363965988159
    epoch_no = 2, batch_index = 15, loss.item() = 0.4766008257865906
    epoch_no = 2, batch_index = 20, loss.item() = 0.45220986008644104
    ++++++++++++++++++++++++++++++++++++++++++++第2轮Epoch-->Training 阶段：结束++++++++++++++++++++++++++++++++++++++++++++
    ++++++++++++++++++++++++++++++++++++++++++++第2轮Epoch-->Evluation 阶段：开始++++++++++++++++++++++++++++++++++++++++++++
    epoch_no = 2, batch_index = 0, val_acc = 0.71875
    epoch_no = 2, batch_index = 5, val_acc = 0.8020833333333334
    ++++++++++++++++++++++++++++++++++++++++++++第2轮Epoch-->Evluation 阶段：结束++++++++++++++++++++++++++++++++++++++++++++
    epoch = 2, best_epoch = 2, best_acc = 0.8076923076923077
    **************************验证模式：结束**************************
    利用整体数据集进行模型的第2轮Epoch迭代结束:**********************************************************************************************************************************
    
    
    利用整体数据集进行模型的第3轮Epoch迭代开始:**********************************************************************************************************************************
    ++++++++++++++++++++++++++++++++++++++++++++第3轮Epoch-->Training 阶段：开始++++++++++++++++++++++++++++++++++++++++++++
    epoch_no = 3, batch_index = 0, loss.item() = 0.6022523641586304
    epoch_no = 3, batch_index = 5, loss.item() = 0.5406889319419861
    epoch_no = 3, batch_index = 10, loss.item() = 0.22856442630290985
    epoch_no = 3, batch_index = 15, loss.item() = 0.5484329462051392
    epoch_no = 3, batch_index = 20, loss.item() = 0.36236143112182617
    ++++++++++++++++++++++++++++++++++++++++++++第3轮Epoch-->Training 阶段：结束++++++++++++++++++++++++++++++++++++++++++++
    ++++++++++++++++++++++++++++++++++++++++++++第3轮Epoch-->Evluation 阶段：开始++++++++++++++++++++++++++++++++++++++++++++
    epoch_no = 3, batch_index = 0, val_acc = 0.84375
    epoch_no = 3, batch_index = 5, val_acc = 0.859375
    ++++++++++++++++++++++++++++++++++++++++++++第3轮Epoch-->Evluation 阶段：结束++++++++++++++++++++++++++++++++++++++++++++
    epoch = 3, best_epoch = 3, best_acc = 0.8589743589743589
    **************************验证模式：结束**************************
    利用整体数据集进行模型的第3轮Epoch迭代结束:**********************************************************************************************************************************
    
    
    利用整体数据集进行模型的第4轮Epoch迭代开始:**********************************************************************************************************************************
    ++++++++++++++++++++++++++++++++++++++++++++第4轮Epoch-->Training 阶段：开始++++++++++++++++++++++++++++++++++++++++++++
    epoch_no = 4, batch_index = 0, loss.item() = 0.47427237033843994
    epoch_no = 4, batch_index = 5, loss.item() = 0.30755600333213806
    epoch_no = 4, batch_index = 10, loss.item() = 0.7977475523948669
    epoch_no = 4, batch_index = 15, loss.item() = 0.3868430554866791
    epoch_no = 4, batch_index = 20, loss.item() = 0.46423253417015076
    ++++++++++++++++++++++++++++++++++++++++++++第4轮Epoch-->Training 阶段：结束++++++++++++++++++++++++++++++++++++++++++++
    ++++++++++++++++++++++++++++++++++++++++++++第4轮Epoch-->Evluation 阶段：开始++++++++++++++++++++++++++++++++++++++++++++
    epoch_no = 4, batch_index = 0, val_acc = 0.90625
    epoch_no = 4, batch_index = 5, val_acc = 0.8958333333333334
    ++++++++++++++++++++++++++++++++++++++++++++第4轮Epoch-->Evluation 阶段：结束++++++++++++++++++++++++++++++++++++++++++++
    epoch = 4, best_epoch = 4, best_acc = 0.8931623931623932
    **************************验证模式：结束**************************
    利用整体数据集进行模型的第4轮Epoch迭代结束:**********************************************************************************************************************************
    best acc: 0.8931623931623932 best epoch: 4
    loaded from ckpt!
    ++++++++++++++++++++++++++++++++++++++++++++第4轮Epoch-->Evluation 阶段：开始++++++++++++++++++++++++++++++++++++++++++++
    epoch_no = 4, batch_index = 0, val_acc = 0.84375
    epoch_no = 4, batch_index = 5, val_acc = 0.828125
    ++++++++++++++++++++++++++++++++++++++++++++第4轮Epoch-->Evluation 阶段：结束++++++++++++++++++++++++++++++++++++++++++++
    test acc: 0.8290598290598291
    
    Process finished with exit code 0

## 三、迁移学习 & 预训练ResNet18 & 自定义数据集-Pytorch ##

import torch
    from torch.utils.data import DataLoader
    from torch import nn, optim
    from torch.nn import functional as F
    import visdom
    import csv
    import glob
    import os
    import random
    from PIL import Image
    from torch.utils.data import Dataset  # 自定义数据集的父类
    from torchvision import transforms
    from torchvision.models import resnet18
    
    torch.manual_seed(1234)  # 随机种子
    device = torch.device('cuda')  # 设置GPU
    
    
    # =============================================================================Pokemon自定义数据集：开始=============================================================================
    class Pokemon(Dataset):
        # root表示数据位置；resize表示数据输出的size；mode表示训练模式/测试模式
        def __init__(self, root, resize, mode):
            super(Pokemon, self).__init__()
            self.root = root
            self.resize = resize
    
            # 给各个类型进行编号
            self.name2label = {
        }  # {'bulbasaur': 0, 'charmander': 1, 'mewtwo': 2, 'pikachu': 3, 'squirtle': 4}
            for name in sorted(os.listdir(os.path.join(root))):
                if not os.path.isdir(os.path.join(root, name)):  # 过滤掉不是文件夹的文件
                    continue
                self.name2label[name] = len(self.name2label.keys())
            print('self.name2label = {0}'.format(self.name2label))  # {'bulbasaur': 0, 'charmander': 1, 'mewtwo': 2, 'pikachu': 3, 'squirtle': 4}
    
            # 读取已保存的图片+标签数据集
            self.img_paths, self.labels = self.load_csv('img_paths.csv')  # 数据对(img_path + image_label)：img_paths, labels
            # 对数据集根据当前模式进行裁剪
            if mode == 'train':  # 60%
                self.img_paths = self.img_paths[:int(0.6 * len(self.img_paths))]
                self.labels = self.labels[:int(0.6 * len(self.labels))]
            elif mode == 'val':  # 20% = 60%->80%
                self.img_paths = self.img_paths[int(0.6 * len(self.img_paths)):int(0.8 * len(self.img_paths))]
                self.labels = self.labels[int(0.6 * len(self.labels)):int(0.8 * len(self.labels))]
            else:  # 20% = 80%->100%
                self.img_paths = self.img_paths[int(0.8 * len(self.img_paths)):]
                self.labels = self.labels[int(0.8 * len(self.labels)):]
    
        def load_csv(self, filename):
            # 1、如果没有csv文件，则创建该csv文件
            if not os.path.exists(os.path.join(self.root, filename)):
                img_paths = []  # 把所有图片的path都保存在该list中，各个图片的label可以从path推断出来，所有没有单独保存。
                for name in self.name2label.keys():
                    img_paths += glob.glob(os.path.join(self.root, name, '*.png'))  # 'pokemon\\mewtwo\\00001.png
                    img_paths += glob.glob(os.path.join(self.root, name, '*.jpg'))
                    img_paths += glob.glob(os.path.join(self.root, name, '*.jpeg'))
                    img_paths += glob.glob(os.path.join(self.root, name, '*.gif'))
                print('len(img_paths) = {0}, img_paths = {1}'.format(len(img_paths), img_paths))  # len(img_paths) = 1168, img_paths = ['pokemon\\bulbasaur\\00000000.png','pokemon\\bulbasaur\\00000001.png',...]
                random.shuffle(img_paths)  # 打乱list中的图片顺序
                # 向csv文件保存图片的path+label
                with open(os.path.join(self.root, filename), mode='w', newline='') as f:
                    writer = csv.writer(f)
                    for img_path in img_paths:  # 'pokemon\\bulbasaur\\00000000.png'
                        name = img_path.split(os.sep)[-2]
                        label = self.name2label[name]
                        writer.writerow([img_path, label])  # 'pokemon\\bulbasaur\\00000000.png', 0
                    print('writen into csv file:', filename)
            # 2、如果已经有csv文件，则读取该csv文件
            img_paths, labels = [], []
            with open(os.path.join(self.root, filename)) as f:
                reader = csv.reader(f)
                for row in reader:
                    img_path, label = row  # 'pokemon\\bulbasaur\\00000000.png', 0
                    label = int(label)
                    img_paths.append(img_path)
                    labels.append(label)
            assert len(img_paths) == len(labels)
            return img_paths, labels
    
        def __len__(self):
            return len(self.img_paths)
    
        def denormalize(self, x_hat):
            mean = [0.485, 0.456, 0.406]
            std = [0.229, 0.224, 0.225]
            # x_hat = (x-mean)/std
            # x = x_hat*std = mean
            # x: [c, h, w]
            # mean: [3] => [3, 1, 1]
            mean = torch.tensor(mean).unsqueeze(1).unsqueeze(1)
            std = torch.tensor(std).unsqueeze(1).unsqueeze(1)
            print('denormalize-->mean.shape = {0}, std.shape = {1}'.format(mean.shape, std.shape))
            x = x_hat * std + mean
    
            return x
    
        def __getitem__(self, img_idx):  # img_idx~[0~len(img_paths)]
            img_path, label = self.img_paths[img_idx], self.labels[img_idx]  # img_path: 'pokemon\\bulbasaur\\00000000.png'；label: 0
            transform = transforms.Compose([
                lambda x: Image.open(x).convert('RGB'),  # string path --> image data
                transforms.Resize((int(self.resize * 1.25), int(self.resize * 1.25))),
                transforms.RandomRotation(15),  # rotate如果比较大的话，可能会造成网络不收敛
                transforms.CenterCrop(self.resize),
                transforms.ToTensor(),
                transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # 该数值是实践中统计的效果比较好的值
            ])
            img = transform(img_path)
            label = torch.tensor(label)
    
            return img, label
    
    
    # =============================================================================Pokemon自定义数据集：结束=============================================================================
    
    class Flatten(nn.Module):
        def __init__(self):
            super(Flatten, self).__init__()
    
        def forward(self, x):
            shape = torch.prod(torch.tensor(x.shape[1:])).item()
            return x.view(-1, shape)
    
    # =============================================================================训练主体：开始=============================================================================
    batch_size = 32
    viz = visdom.Visdom()  # 在控制台开启Visdom：python -m visdom.server
    global_step = 0
    
    # 一、获取Pokemon训练数据集
    train_db = Pokemon('pokemon', 224, mode='train')
    val_db = Pokemon('pokemon', 224, mode='val')
    test_db = Pokemon('pokemon', 224, mode='test')
    train_loader = DataLoader(train_db, batch_size=batch_size, shuffle=True, num_workers=0)  # num_workers表示开启的线程数量
    val_loader = DataLoader(val_db, batch_size=batch_size, num_workers=0)
    test_loader = DataLoader(test_db, batch_size=batch_size, num_workers=0)
    
    # 三、实例化预训练ResNet18神经网络模型
    trained_model = resnet18(pretrained=True)
    model = nn.Sequential(*list(trained_model.children())[:-1],  # 提取已经训练好的resnet18模型的前17层，打散。[b, 512, 1, 1]
                          Flatten(),  # [b, 512, 1, 1] => [b, 512]
                          nn.Linear(512, 5)
                          ).to(device)
    # Find total parameters and trainable parameters
    total_params = sum(p.numel() for p in model.parameters())
    print(f'{total_params:,} total parameters.')
    total_trainable_params = sum(
        p.numel() for p in model.parameters() if p.requires_grad)
    print(f'{total_trainable_params:,} training parameters.')
    print('model = {0}\n'.format(model))
    
    # 四、实例化损失函数
    criteon = nn.CrossEntropyLoss().to(device)
    
    # 五、梯度下降优化器设置
    optimizer = optim.Adam(model.parameters(), lr=1e-3)
    
    
    def train_epoch(epoch_no):
        global global_step
        print('++++++++++++++++++++++++++++++++++++++++++++第{0}轮Epoch-->Training 阶段：开始++++++++++++++++++++++++++++++++++++++++++++'.format(epoch_no))
        model.train()  # 切换至训练模式
        for batch_index, (X_batch, Y_batch) in enumerate(train_loader):
            X_batch, Y_batch = X_batch.to(device), Y_batch.to(device)
            out_logits = model(X_batch)
            loss = criteon(out_logits, Y_batch)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            viz.line([loss.item()], [global_step], win='loss', update='append')
            global_step += 1
            if batch_index % 5 == 0:
                print('epoch_no = {0}, batch_index = {1}, loss.item() = {2}'.format(epoch_no, batch_index, loss.item()))
        print('++++++++++++++++++++++++++++++++++++++++++++第{0}轮Epoch-->Training 阶段：结束++++++++++++++++++++++++++++++++++++++++++++'.format(epoch_no))
    
    
    def evalute(epoch_no, loader):
        print('++++++++++++++++++++++++++++++++++++++++++++第{0}轮Epoch-->Evluation 阶段：开始++++++++++++++++++++++++++++++++++++++++++++'.format(epoch_no))
        model.eval()
        with torch.no_grad():
            total_correct = 0
            total_num = 0
            for batch_index, (X_batch, Y_batch) in enumerate(loader):
                X_batch, Y_batch = X_batch.to(device), Y_batch.to(device)
                out_logits = model(X_batch)
                out_pred = out_logits.argmax(dim=1)
                correct = torch.eq(out_pred, Y_batch).float().sum().item()
                total_correct += correct
                total_num += X_batch.size(0)
                val_acc = total_correct / total_num
                viz.line([val_acc], [global_step], win='val_acc', update='append')
                if batch_index % 5 == 0:
                    print('epoch_no = {0}, batch_index = {1}, val_acc = {2}'.format(epoch_no, batch_index, val_acc))
        print('++++++++++++++++++++++++++++++++++++++++++++第{0}轮Epoch-->Evluation 阶段：结束++++++++++++++++++++++++++++++++++++++++++++'.format(epoch_no))
        return val_acc
    
    
    def main():
        epoch_count = 4  # epoch_count为整体数据集迭代梯度下降次数
        best_acc, best_epoch = 0, 0
        viz.line([0], [-1], win='loss', opts=dict(title='loss'))
        viz.line([0], [-1], win='val_acc', opts=dict(title='val_acc'))
        for epoch_no in range(1, epoch_count + 1):
            print('\n\n利用整体数据集进行模型的第{0}轮Epoch迭代开始:**********************************************************************************************************************************'.format(epoch_no))
            train_epoch(epoch_no)  # 训练
            val_acc = evalute(epoch_no, val_loader)  # 验证
            if val_acc > best_acc:
                best_epoch = epoch_no
                best_acc = val_acc
                torch.save(model.state_dict(), 'best.mdl')
            print('epoch = {0}, best_epoch = {1}, best_acc = {2}'.format(epoch_no, best_epoch, best_acc))
            print('**************************验证模式：结束**************************')
            print('利用整体数据集进行模型的第{0}轮Epoch迭代结束:**********************************************************************************************************************************'.format(epoch_no))
        print('best acc:', best_acc, 'best epoch:', best_epoch)
        model.load_state_dict(torch.load('best.mdl'))
        print('loaded from ckpt!')
        test_acc = evalute(best_epoch, test_loader)  # 测试
        print('test acc:', test_acc)
    
    
    if __name__ == '__main__':
        main()
    
    # =============================================================================训练主体：结束=============================================================================

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE_size_16_color_FFFFFF_t_70_pic_center 1]  
打印结果：

Setting up a new session...
    self.name2label = {
        'bulbasaur': 0, 'charmander': 1, 'mewtwo': 2, 'pikachu': 3, 'squirtle': 4}
    self.name2label = {
        'bulbasaur': 0, 'charmander': 1, 'mewtwo': 2, 'pikachu': 3, 'squirtle': 4}
    self.name2label = {
        'bulbasaur': 0, 'charmander': 1, 'mewtwo': 2, 'pikachu': 3, 'squirtle': 4}
    11,179,077 total parameters.
    11,179,077 training parameters.
    model = Sequential(
      (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
      (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
      (4): Sequential(
        (0): BasicBlock(
          (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
        (1): BasicBlock(
          (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (5): Sequential(
        (0): BasicBlock(
          (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (downsample): Sequential(
            (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
            (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): BasicBlock(
          (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (6): Sequential(
        (0): BasicBlock(
          (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (downsample): Sequential(
            (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
            (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): BasicBlock(
          (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (7): Sequential(
        (0): BasicBlock(
          (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (downsample): Sequential(
            (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
            (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          )
        )
        (1): BasicBlock(
          (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (relu): ReLU(inplace=True)
          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (8): AdaptiveAvgPool2d(output_size=(1, 1))
      (9): Flatten()
      (10): Linear(in_features=512, out_features=5, bias=True)
    )
    
    
    
    利用整体数据集进行模型的第1轮Epoch迭代开始:**********************************************************************************************************************************
    ++++++++++++++++++++++++++++++++++++++++++++第1轮Epoch-->Training 阶段：开始++++++++++++++++++++++++++++++++++++++++++++
    epoch_no = 1, batch_index = 0, loss.item() = 1.664962887763977
    epoch_no = 1, batch_index = 5, loss.item() = 0.4224851131439209
    epoch_no = 1, batch_index = 10, loss.item() = 0.3056411147117615
    epoch_no = 1, batch_index = 15, loss.item() = 0.6770390868186951
    epoch_no = 1, batch_index = 20, loss.item() = 0.778434157371521
    ++++++++++++++++++++++++++++++++++++++++++++第1轮Epoch-->Training 阶段：结束++++++++++++++++++++++++++++++++++++++++++++
    ++++++++++++++++++++++++++++++++++++++++++++第1轮Epoch-->Evluation 阶段：开始++++++++++++++++++++++++++++++++++++++++++++
    epoch_no = 1, batch_index = 0, val_acc = 0.875
    epoch_no = 1, batch_index = 5, val_acc = 0.7239583333333334
    ++++++++++++++++++++++++++++++++++++++++++++第1轮Epoch-->Evluation 阶段：结束++++++++++++++++++++++++++++++++++++++++++++
    epoch = 1, best_epoch = 1, best_acc = 0.7136752136752137
    **************************验证模式：结束**************************
    利用整体数据集进行模型的第1轮Epoch迭代结束:**********************************************************************************************************************************
    
    
    利用整体数据集进行模型的第2轮Epoch迭代开始:**********************************************************************************************************************************
    ++++++++++++++++++++++++++++++++++++++++++++第2轮Epoch-->Training 阶段：开始++++++++++++++++++++++++++++++++++++++++++++
    epoch_no = 2, batch_index = 0, loss.item() = 0.5391928553581238
    epoch_no = 2, batch_index = 5, loss.item() = 0.641627848148346
    epoch_no = 2, batch_index = 10, loss.item() = 0.28850072622299194
    epoch_no = 2, batch_index = 15, loss.item() = 0.44357800483703613
    epoch_no = 2, batch_index = 20, loss.item() = 0.15881212055683136
    ++++++++++++++++++++++++++++++++++++++++++++第2轮Epoch-->Training 阶段：结束++++++++++++++++++++++++++++++++++++++++++++
    ++++++++++++++++++++++++++++++++++++++++++++第2轮Epoch-->Evluation 阶段：开始++++++++++++++++++++++++++++++++++++++++++++
    epoch_no = 2, batch_index = 0, val_acc = 0.65625
    epoch_no = 2, batch_index = 5, val_acc = 0.7447916666666666
    ++++++++++++++++++++++++++++++++++++++++++++第2轮Epoch-->Evluation 阶段：结束++++++++++++++++++++++++++++++++++++++++++++
    epoch = 2, best_epoch = 2, best_acc = 0.7478632478632479
    **************************验证模式：结束**************************
    利用整体数据集进行模型的第2轮Epoch迭代结束:**********************************************************************************************************************************
    
    
    利用整体数据集进行模型的第3轮Epoch迭代开始:**********************************************************************************************************************************
    ++++++++++++++++++++++++++++++++++++++++++++第3轮Epoch-->Training 阶段：开始++++++++++++++++++++++++++++++++++++++++++++
    epoch_no = 3, batch_index = 0, loss.item() = 0.11576351523399353
    epoch_no = 3, batch_index = 5, loss.item() = 0.10171618312597275
    epoch_no = 3, batch_index = 10, loss.item() = 0.19451947510242462
    epoch_no = 3, batch_index = 15, loss.item() = 0.06140638515353203
    epoch_no = 3, batch_index = 20, loss.item() = 0.049921028316020966
    ++++++++++++++++++++++++++++++++++++++++++++第3轮Epoch-->Training 阶段：结束++++++++++++++++++++++++++++++++++++++++++++
    ++++++++++++++++++++++++++++++++++++++++++++第3轮Epoch-->Evluation 阶段：开始++++++++++++++++++++++++++++++++++++++++++++
    epoch_no = 3, batch_index = 0, val_acc = 0.96875
    epoch_no = 3, batch_index = 5, val_acc = 0.953125
    ++++++++++++++++++++++++++++++++++++++++++++第3轮Epoch-->Evluation 阶段：结束++++++++++++++++++++++++++++++++++++++++++++
    epoch = 3, best_epoch = 3, best_acc = 0.9487179487179487
    **************************验证模式：结束**************************
    利用整体数据集进行模型的第3轮Epoch迭代结束:**********************************************************************************************************************************
    
    
    利用整体数据集进行模型的第4轮Epoch迭代开始:**********************************************************************************************************************************
    ++++++++++++++++++++++++++++++++++++++++++++第4轮Epoch-->Training 阶段：开始++++++++++++++++++++++++++++++++++++++++++++
    epoch_no = 4, batch_index = 0, loss.item() = 0.08163614571094513
    epoch_no = 4, batch_index = 5, loss.item() = 0.1351318359375
    epoch_no = 4, batch_index = 10, loss.item() = 0.06922706216573715
    epoch_no = 4, batch_index = 15, loss.item() = 0.051600512117147446
    epoch_no = 4, batch_index = 20, loss.item() = 0.05538956820964813
    ++++++++++++++++++++++++++++++++++++++++++++第4轮Epoch-->Training 阶段：结束++++++++++++++++++++++++++++++++++++++++++++
    ++++++++++++++++++++++++++++++++++++++++++++第4轮Epoch-->Evluation 阶段：开始++++++++++++++++++++++++++++++++++++++++++++
    epoch_no = 4, batch_index = 0, val_acc = 0.90625
    epoch_no = 4, batch_index = 5, val_acc = 0.9479166666666666
    ++++++++++++++++++++++++++++++++++++++++++++第4轮Epoch-->Evluation 阶段：结束++++++++++++++++++++++++++++++++++++++++++++
    epoch = 4, best_epoch = 3, best_acc = 0.9487179487179487
    **************************验证模式：结束**************************
    利用整体数据集进行模型的第4轮Epoch迭代结束:**********************************************************************************************************************************
    best acc: 0.9487179487179487 best epoch: 3
    loaded from ckpt!
    ++++++++++++++++++++++++++++++++++++++++++++第3轮Epoch-->Evluation 阶段：开始++++++++++++++++++++++++++++++++++++++++++++
    epoch_no = 3, batch_index = 0, val_acc = 0.96875
    epoch_no = 3, batch_index = 5, val_acc = 0.921875
    ++++++++++++++++++++++++++++++++++++++++++++第3轮Epoch-->Evluation 阶段：结束++++++++++++++++++++++++++++++++++++++++++++
    test acc: 0.9230769230769231
    
    Process finished with exit code 0

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE_size_16_color_FFFFFF_t_70_pic_center]: https://img-blog.csdnimg.cn/20210121224431976.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE=,size_16,color_FFFFFF,t_70#pic_center
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE_size_16_color_FFFFFF_t_70_pic_center 1]: https://img-blog.csdnimg.cn/20210121224916280.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3UwMTMyNTA4NjE=,size_16,color_FFFFFF,t_70#pic_center